Ozone Day AdaBoostClassifier and Random Forest Tree Classifier with Machine Learning

I have developed 2 machine learning software that predict and classify ozone day and non ozone day. I upgraded and improved the accuracy values of one of these software. The data was unbalanced, and I painstakingly worked to reduce this imbalance. I created 2 classification software with 109 trees. One is the auxiliary classification and the other is the main classification. I am sharing the classification of 109 trees as .dot. Continuing mind-opening projects in the field of machine learning!

Example: model_ozone = ensemble.AdaBoostClassifier(base_estimator=None, n_estimators=109, learning_rate=1.549999999999978, algorithm='SAMME.R', )

#Auc Roc Curve Score: 0.7499689633767846

#Precision Score: 85.58992487847989

#Recall Score: 74.99689633767845

#F1 Score: 79.2258134963966

#Accuracy: 0.9605781865965834

#[[708 8] [ 22 23]]

I am happy to present this software to you!

Data Source: DataSource ###The coding language used:

Python 3.9.6

###Libraries Used:

Sklearn

Pandas

Numpy

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan.bulut@turkiyeyapayzeka.com

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Official Website: https://www.emirhanbulut.com.tr